کرایه دستمزد برای سرمایه های انسانی تجدید پذیر: شواهد از غنا و ساحل عاج

Education, child nutrition, adult health/nutrition, and labor mobility are critical factors in achieving recent sustained growth in factor productivity. To compare the contribution of these four human capital inputs, an expanded specification of the wage function is estimated from household (LSMS) surveys of the Ivory Coast and Ghana. Specification tests assess whether the human capital inputs are exogenous, and instrumental variable techniques are used to estimate the wage function. Smaller panels from the Ivory Coast imply the magnitude of measurement error in the human capital inputs and provide more efficient instruments to estimate the wage equation. The conclusion emerges that weight-for-height and height are endogenous, particularly prone to measurement error, and heterogeneous in their effects on wages. Overall returns to these four forms of human capital are similar within each country for men and women, but education and migration returns are higher in the more rapidly growing Ivory Coast, and the wage effects of child nutrition proxied by height are greater in poorer, more malnourished Ghana.

Schooling, height, weight-for-height, and migration are attributes of workers associated with their current productivity. These forms of worker heterogeneity are to some degree reproducible: schooling and migration are created by well-described processes, whereas height and weight-for-height are formed by the biological process of human growth, in which the inputs of nutritional intakes, protection from exposure to disease, health care, and activity levels combine to exert a net cumulative effect on the individual’s realization of their genetic potential. The impact of height and weight-for-height on labor productivity and well-being have been extensively documented in by economic historians (Fogel, 1994; Steckel, 1995), and more recently studied in contemporary random surveys from low-income populations (Strauss and Thomas, 1995). These worker attributes are viewed here as indicators of human capital because they can be augmented by social or private investments, but they also vary across individuals because of genetic and environmental factors that are not controlled by the individual, family, or society. This paper estimates the productive payoff to the formation of these four human capital stocks in two low-income countries.1 Because the cost of creating these stocks has not been accounted for, only estimates of the wage rental values of these stocks are offered here and not internal rates of return. Several questions are addressed. First, how important for labor productivity is each of these four dimensions of worker heterogeneity considered jointly, for men and women separately, in two Sub-Saharan African countries where the conditions of health and nutrition are relatively poor?2 Second, do the wage payoffs to forms of human capital change when one allows for human capital stocks to be endogenous, heterogenous, and measured with random error. Finally, how do these forms of human capital interact in their determination of worker productive capacity; can complementarity between forms of human capital (interactions) be distinguished from changing “returns to scale”. In Section 2, a simple framework is outlined for guiding the estimation of an extended wage function that includes several, possibly endogenous, heterogenous, and measured with error, human capital stocks. The data are described in Section 3. Empirical specification issues are discussed further in Section 4. 5 and 6 report the estimates for cross-sectional surveys from the Ivory Coast and Ghana. Then in Section 7, for a smaller 2-year panel from adjacent years of the Ivory Coast surveys, measurement error is quantified and alternative estimates of wage functions are compared. Section 8 presents flexible form estimates to assess nonlinearities, and Section 9 reconsiders the gender wage gap in terms of the human capital inputs. Section 10 summarizes the new evidence and suggests how further research might resolve some of the outstanding questions.

The effects on log wages of four measures of human capital—height proxying childhood nutritional status, education, migration, and body-mass-index proxying adult health—are estimated here for men and women separately for the Ivory Coast and Ghana from household surveys collected during the late 1980s. These four human capital inputs are initially treated as measured without error, homogenous, and exogenous. Under these working assumptions, OLS estimates are unbiased and efficient, and they confirm what other studies have found: wage differentials associated with education are substantial in the Ivory Coast and moderate in Ghana, which can be explained by both the relatively larger supply of educated workers in Ghana and the relatively slow growth of the national economy from 1960 to 1990 in Ghana compared with the Ivory Coast. Wage returns are large for migration in the Ivory Coast and Ghana, substantial for BMI in all four samples, and for height in all samples except women in the Ivory Coast. The first finding is then that the estimated wage returns to schooling are reduced by 10–20% with the addition to the wage function of these other three key human capital inputs (Table 4, Columns (1)–(4)). Although the conventional assumption in the wage function literature is that human capital inputs are exogenous, Hausman tests of this specification choice indicate that the exogeneity of height and BMI is more often rejected than not, whereas the exogeneity of migration and education cannot be rejected in more than one out of the four samples. The biologically fixed variation in height and BMI may exert smaller effects on labor productivity than does the human capital induced variation in these measures of stature, contributing to the Hausman test rejecting the equality of the effect of overall variation in stature on wages compared with the effect of the reproducible variation in stature explained by the instruments (Schultz, 2002). Schooling is relatively well explained by the availability of schools and parent education, providing powerful instruments which imply schooling selection is not a source of bias in estimating educational returns. Migration exerts a large and uncertain effect on wages, and the instruments may not be sufficiently powerful in explaining the human capital component of migration to distinguish between the effect of the random and human capital component of migration on wages. Instrumental variable estimates are reported based on the assumption that the health/school infrastructure, food prices of the local childhood community, and the parent’s education and occupation influence the household’s demand for these four human capital inputs, but that these instruments do not enter the wage equation. In addition to obtaining IV estimates in Table 4, Column (5) assuming all four inputs are endogenous, the preferred IV estimates in Column (6) rely on the Hausman tests and assume that education and migration are exogenous, and only height and BMI are estimated as endogenous. The notable finding in Table 4 is that the IV estimates of the productive payoff to BMI and height are larger than the OLS estimates in Ghana the poorer and less well nourished country, and they are also larger in Cote d’Ivoire for BMI, if not for height. There are at least three possible explanations for this result. The errors in measuring BMI and height are larger than those in measuring education and migration, or heterogeneity in individuals and families accounts for an omitted variable bias, or the productive consequences of reproducible and innate components of human capital differ and the instrumental variable estimates approximate the payoff to the reproducible component which exceeds the returns to the unexplained genotypic variation in stature. To distinguish among these alternative hypotheses for the surprising cross-sectional IV estimates, smaller panels of repeated observations on the same individual are analyzed from the Ivory Coast.32 If errors in measurement of each human capital input are independent over time, and uncorrelated with other errors or with other control variables, averaging of adjacent years of the human capital inputs should decrease by half the attenuation bias caused by the random measurement error. Consistent with this “classical” framework, the panel estimates of the wage effects based on the 2-year average values increase marginally for education but increase 10–30% for BMI and height (Table 8, Column (2) versus Column (3)). This pattern of IV estimates confirms much larger errors in measurement for the anthropometric indicators than for education (Table 8, Column (2) versus Column (4)). The second approach is to estimate by instrumental variables the human capital effects on wages, using the adjacent year’s value of the input to predict the current input’s value. These IV estimates for BMI and height are about 50% larger than those obtained by OLS, whereas those for education increase by less then 10% (Table 8, Column (2) versus Column (5)). The third approach to the panel is to use the local community food prices, health and schooling infrastructure in the region of birth, and parent characteristics to instrument for the human capital inputs, as in the larger cross-section. The IV estimates for BMI increase further for women and are of the same magnitude for men as they were for the prior IV estimates, whereas the estimates of height for which the instruments are weakest lose their statistical significance. The panel evidence reaffirms that the returns to education and migration are not substantially biased by the assumption that these forms of human capital are measured without error and are exogenously determined. BMI and height will require much further study as potentially heterogeneous and endogenous inputs to the wage function, which are also subject to substantial amounts of measurement error in these surveys. Extending the analysis to a comparison of men and women, a form of the Oaxaca wage decomposition of the gender wage gap can be performed with the OLS estimates in Table 10. In the Ivory Coast, the gender wage gap is wider, with men receiving wages that are 58% larger than those of women. Three-fifths of this gap is accounted for by the differences in the four human capital endowments of men and women, weighting them by the wage function coefficients averaged for both sexes. In Ghana, the wage gap is 23%, and the human capital inputs account for four-fifths of the gap (Table 10). Returns to the human capital inputs are expected to vary with the scale of investment, and interactions between all pairs of inputs need not be uniformly complementary as implied in the standard semilog-linear specification of the wage function. A more flexible functional form is therefore estimated (Table 9). Yet, the demands on these data to define this second-order approximation of the wage function may be excessive, for few strong empirical regularities emerge, except that returns to schooling increase after primary schooling, which is confirmed by other studies of these countries. What specific programs, policies, and relative prices in a local community encourage greater investments in the four human capital inputs distinguished in this paper? Community questionnaires could be better focused and more useful for policy analysts, if they knew how to intervene with public resources to increase the quantity of human capital demanded by families. Large household surveys of workers might be more valuable, if they collected not only information on adult education and wages, but also height, weight, and migration histories. Reducing measurement error in collecting the adult anthropometrics should be a priority, and describing policy relevant features of the respondent’s childhood home will require retrospective instruments. Extended wage functions may then be routinely estimated and become a more reliable tool for setting human resource priorities. Thomas and Strauss (1997) have made an innovative start at this type of research for Brazil, but their assumption that height is exogenous in the wage function should be reappraised, and migration histories could be exploited to synchronize instrumental variables to capture more precisely the conditions at birth and during adolescent development. Economic historians have interpreted the relationships between anthropometric indicators of stature and productivity, health, and welfare (Fogel, 1994). The empirical study of wage functions in low-income countries may now refine these historical insights, extend the framework of Mincer (1994) to accommodate a richer portfolio of human capital, and model explicitly the household’s demands for various forms of reproducible human capital.